Run
10560421

Run 10560421

Task 167141 (Supervised Classification) churn Uploaded 13-08-2021 by Sergey Redyuk
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer, pca=sklearn.decomposition.pca.PCA,gradientboostingclassifier=sklearn.ensemb le.gradient_boosting.GradientBoostingClassifier)(2)Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting to None.
sklearn.decomposition.pca.PCA(11)_copytrue
sklearn.decomposition.pca.PCA(11)_iterated_power"auto"
sklearn.decomposition.pca.PCA(11)_n_componentsnull
sklearn.decomposition.pca.PCA(11)_random_state7681
sklearn.decomposition.pca.PCA(11)_svd_solver"auto"
sklearn.decomposition.pca.PCA(11)_tol0.0
sklearn.decomposition.pca.PCA(11)_whitenfalse
sklearn.preprocessing.imputation.Imputer(52)_axis0
sklearn.preprocessing.imputation.Imputer(52)_copytrue
sklearn.preprocessing.imputation.Imputer(52)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(52)_strategy"median"
sklearn.preprocessing.imputation.Imputer(52)_verbose0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_criterion"friedman_mse"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_initnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_learning_rate0.05
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_loss"deviance"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_max_depth6
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_max_featuresnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_max_leaf_nodesnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_min_impurity_split1e-07
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_min_samples_leaf1
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_min_samples_split2
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_min_weight_fraction_leaf0.0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_n_estimators50
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_presort"auto"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_random_state38605
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_subsample0.5
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_verbose0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_warm_startfalse
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,pca=sklearn.decomposition.pca.PCA,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "pca", "step_name": "pca"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "gradientboostingclassifier", "step_name": "gradientboostingclassifier"}}]

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

18 Evaluation measures

Area under ROC curve
0.9222 ± 0.0181
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9464 ± 0.0122
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7677 ± 0.0542
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.5989 ± 0.0508
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0991 ± 0.0085
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2429 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9496 ± 0.0112
Cross-validation details (10-fold Crossvalidation)
Number of instances
5000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9489 ± 0.0123
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9496 ± 0.0112
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.5879 ± 0.0025
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4078 ± 0.035
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3484 ± 0.001
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2057 ± 0.0177
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.5904 ± 0.0508
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.843 ± 0.029
Cross-validation details (10-fold Crossvalidation)